Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human–Machine Interfaces

Abstract Recent advances in flexible wearable devices have boosted the remarkable development of devices for human–machine interfaces, which are of great value to emerging cybernetics, robotics, and Metaverse systems. However, the effectiveness of existing approaches is limited by the quality of sensor data and classification models with high computational costs. Here, a novel gesture recognition system with triboelectric smart wristbands and an adaptive accelerated learning (AAL) model is proposed. The sensor array is well deployed according to the wrist anatomy and retrieves hand motions from a distance, exhibiting highly sensitive and high‐quality sensing capabilities beyond existing methods. Importantly, the anatomical design leads to the close correspondence between the actions of dominant muscle/tendon groups and gestures, and the resulting distinctive features in sensor signals are very valuable for differentiating gestures with data from 7 sensors. The AAL model realizes a 97.56% identification accuracy in training 21 classes with only one‐third operands of the original neural network. The applications of the system are further exploited in real‐time somatosensory teleoperations with a low latency of <1 s, revealing a new possibility for endowing cyber‐human interactions with disruptive innovation and immersive experience.

. Human hand musculature. The hand is articulated by numerous muscle networks that cover the entire forearm, exhibiting complex paths, especially around the wrist, wrapping around multiple moving muscles and bones. Therefore, hand gestures are difficult to be estimated. Reproduced with permission from REF. 1 , Springer Nature.  As the induced charges on LIG electrode gradually decrease, the electrons flow to the LIG electrode from the ground in the external circuit system. State Ⅲ: When the muscle contraction reaches its maximum, the induced charges on the electrode reduce to almost zero. State Ⅳ: When the muscle gradually releases, there is an opposite electron flow in the external circuit system until the muscle reaches its initial condition State Ⅰ.

Note S1. The grasp taxonomy of different grasping gestures
The grasp classification in this work depends not only on the hand pose, but also on the shape of the object and the type of opposition between hand and object. For instance, "ball" (#1) and "cup" (#2) gestures have a similar handshape and object size, but the object in the former is a prism, while that in the latter is a uniform sphere. Therefore, the finger bending degree will be different. Besides, the thumb carpometacarpal (CMC) joint is adducted only in "screwdriver" (#3) and "hook"

Note S2. Setup of the data training process
For the data training process, we use stochastic gradient descent (SGD) with a data batch size from 300 to 800 with an internal of 100 to generate different model configurations, and an epoch number of 80 for iteratively updating model parameters, such as the gate parameter θ for determining filter pruning. In order to accelerate the training procedure, the learning rate is reselected every 10 epochs. To prevent the training model from overfitting, a weight decay of 0.0001 is used.

Note S3. Method of updating discrete gate parameters
The GPP strategy is used to ensure discrete gates with micro gate parameters can also be reactivated as candidate channels again. Specifically, the GPP strategy for optimizing sub-network is guided by the macro-control of the loss function pr L , which is defined as: i j  is learnable according to the identification performances in the iterative process-that is, if the removal of a channel leads to a poor recognition accuracy, which means this channel could be important, i j  will be increased and this channel is more possible to be sampled in the next iteration; In contrary, if the removal of a channel leads to a desirable recognition accuracy, i j  will be decreased and this channel is more possible to be pruned in the next iteration.
The multiplication of   i j g  and the corresponding channel output is used as the 18 final output of the channel in the sub-network. As a result, a specific pruning rate can be obtained by minimizing pr L . The opened discrete gates will not affect the corresponding parameters trained in the original network, while the parameters associated with the closed discrete gates will not participate in training.